AWhat changed
Replaced uniform residual accumulation with learned input-dependent attention over earlier layer outputs, with a blockwise variant and communication/computation strategy for scalable training.
Scientific scope
Replaced uniform residual accumulation with learned input-dependent attention over earlier layer outputs, with a blockwise variant and communication/computation strategy for scalable training. A depth-wise representation routing method, distinct from token attention and DeepNorm initialization. Empirical scaling improvements are not a general optimization/convergence guarantee; report-front collective controls attribution.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. A depth-wise representation routing method, distinct from token attention and DeepNorm initialization. Empirical scaling improvements are not a general optimization/convergence guarantee; report-front collective controls attribution.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Attention Residuals
- Kimi Team: attention-weighted residual aggregation across depth — official method source
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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